Cascade Ranking for Operational E-commerce Search

Fei Xiao, Shichen Liu, Wenwu Ou, Luo Si

Abstract:

In the “Big Data” era, many real-world applications like search
involve the ranking problem for a large number of items. It is
important to obtain effective ranking results and at the same time
obtain the results efficiently in a timely manner for providing good
user experience and saving computational costs. Valuable prior
research has been conducted for learning to efficiently rank like
the cascade ranking (learning) model, which uses a sequence of
ranking functions to progressively filter some items and rank the
remaining items. However, most existing research of learning to
efficiently rank in search is studied in a relatively small computing
environments with simulated user queries.
This paper presents novel research and thorough study of design-
ing and deploying a Cascade model in a Large-scale Operational E-
commerce Search application (CLOES), which deals with hundreds
of millions of user queries per day with hundreds of servers. The
challenge of the real-world application provides new insights for
research: 1). Real-world search applications often involve multiple
factors of preferences or constraints with respect to user experience
and computational costs such as search accuracy, search latency,
size of search results and total CPU cost, while most existing search
solutions only address one or two factors; 2). Effectiveness of e-
commerce search involves multiple types of user behaviors such as
click and purchase, while most existing cascade ranking in search
only models the click behavior. Based on these observations, a novel
cascade ranking model is designed and deployed in an operational
e-commerce search application. An extensive set of experiments
demonstrate the advantage of the proposed work to address multi-
ple factors of effectiveness, efficiency and user experience in the
real-world application.